A deep learning method for image‐based subject‐specific local SAR assessment

Purpose Local specific absorption rate (SAR) cannot be measured and is usually evaluated by offline numerical simulations using generic body models that of course will differ from the patient's anatomy. An additional safety margin is needed to include this intersubject variability. In this work...

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Vydáno v:	Magnetic resonance in medicine Ročník 83; číslo 2; s. 695 - 711
Hlavní autoři:	Meliadò, E.F., Raaijmakers, A.J.E, Sbrizzi, A., Steensma, B.R., Maspero, M., Savenije, M.H.F., Luijten, P.R., Berg, C.A.T.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Wiley Subscription Services, Inc 01.02.2020 John Wiley and Sons Inc
Témata:	Algorithms Artificial neural networks Computer Simulation convolutional neural network Databases, Factual Deep Learning Full Papers—Computer Processing and Modeling Healthy Volunteers Humans Image Processing, Computer-Assisted - methods In vivo methods and tests Machine learning Magnetic Resonance Imaging Male Mathematical models Models, Statistical Neural networks Neural Networks, Computer parallel transmit Phantoms, Imaging Prostate Prostate - diagnostic imaging Prostatic Neoplasms - diagnostic imaging Qualitative analysis Reproducibility of Results Safety factors Safety margins Signal-To-Noise Ratio specific absorption rate subject‐specific SAR assessment Teaching methods ultrahigh‐field MRI deep learning ultrahigh-field MRI specific absorption rate parallel transmit subject-specific SAR assessment convolutional neural network
ISSN:	0740-3194, 1522-2594, 1522-2594
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Popis
Shrnutí:	Purpose Local specific absorption rate (SAR) cannot be measured and is usually evaluated by offline numerical simulations using generic body models that of course will differ from the patient's anatomy. An additional safety margin is needed to include this intersubject variability. In this work, we present a deep learning–based method for image‐based subject‐specific local SAR assessment. We propose to train a convolutional neural network to learn a “surrogate SAR model” to map the relation between subject‐specific B1+ maps and the corresponding local SAR. Method Our database of 23 subject‐specific models with an 8–transmit channel body array for prostate imaging at 7 T was used to build 5750 training samples. These synthetic complex B1+ maps and local SAR distributions were used to train a conditional generative adversarial network. Extra penalization for local SAR underestimation errors was included in the loss function. In silico and in vivo validation were performed. Results In silico cross‐validation shows a good qualitative and quantitative match between predicted and ground‐truth local SAR distributions. The peak local SAR estimation error distribution shows a mean overestimation error of 15% with 13% probability of underestimation. The higher accuracy of the proposed method allows the use of less conservative safety factors compared with standard procedures. In vivo validation shows that the method is applicable with realistic measurement data with impressively good qualitative and quantitative agreement to simulations. Conclusion The proposed deep learning method allows online image‐based subject‐specific local SAR assessment. It greatly reduces the uncertainty in current state‐of‐the‐art SAR assessment methods, reducing the time in the examination protocol by almost 25%.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0740-3194 1522-2594 1522-2594
DOI:	10.1002/mrm.27948